Trimester-aware yoga video recommendation using hybrid deep learning for pregnant woman

Practicing yoga during pregnancy offers significant physical and mental health benefits for expectant mothers. When incorporated into daily routines, yoga can help mitigate common pregnancy-related discomforts and promote overall well-being. Importantly, trimester-aware yoga recommendations enable women to practice safely according to their stage of pregnancy, preparing both body and mind for a healthier gestational period and childbirth.This study proposes a novel trimester-aware hybrid deep learning framework that integrates multimodal text-video analysis with physiological safety reasoning. The model introduces two key innovations: (i) a Trimester-Weighted Wasserstein Similarity (TW-WD) mechanism for adaptive alignment of user and video embeddings under trimester-specific safety constraints, and (ii) a Safety-Aware Directed Graph Convolutional Relational Neural Network (GCRNN) that propagates health-condition and trimester relationships across the recommendation graph. Experimental results demonstrate an accuracy of 98.3% under fivefold cross-validation and over 97.5% trimester-specific safety compliance, highlighting the framework's clinical reliability and effectiveness in delivering personalized prenatal yoga recommendations.